The ability to decide between multiple fixation targets in complex visual environments is essential for our survival. Evolution has refined this process to be both rapid and cheap, allowing us to perform over 100,000 saccades a day. Previous models for visual decision making have focused on maximizing reward magnitude or expected value (EV = probability of reward × magnitude of reward). However, such methods fail to incorporate utility, or happiness derived from reward, optimizing strictly on nominal reward values. We propose an alternative model for visual decision making, maximizing utility as opposed to value under the assumption of a decreasing marginal utility curve. To test our model, we asked 10 UCSD graduate students to participate in an eyetracking experiment where they choose between different fixation targets presented on a brief display. The reward for each target was generated from fixed, predetermined distributions with different variance that was initially unknown to the subjects. The subjects were asked to maximize their reward for each test session within the experiment. Comparing our results with expected value and reward optimizing hedge algorithms, we show that utility-based models more accurately reflect human behavior in visual decision making tasks.